Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer's disease
Journal article, 2024
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.
Author
Lingqi Meng
Royal Institute of Technology (KTH)
Han Jin
Royal Institute of Technology (KTH)
Burak Yulug
Alanya Alaaddin Keykubat University
Ozlem Altay
Royal Institute of Technology (KTH)
Xiangyu Li
Royal Institute of Technology (KTH)
Lutfu Hanoglu
Istanbul Medipol Universitesi
Seyda Cankaya
Alanya Alaaddin Keykubat University
Ebru Coskun
Istanbul Medipol Universitesi
Ezgi Idil
Alanya Alaaddin Keykubat University
Rahim Nogaylar
Alanya Alaaddin Keykubat University
Ahmet Ozsimsek
Alanya Alaaddin Keykubat University
Saeed Shoaie
King's College London
Hasan Turkez
Atatürk University
Jens B Nielsen
Chalmers, Life Sciences, Systems and Synthetic Biology
Cheng Zhang
Royal Institute of Technology (KTH)
Jan Borén
University of Gothenburg
Mathias Uhlen
Royal Institute of Technology (KTH)
Adil Mardinoglu
Royal Institute of Technology (KTH)
King's College London
Alzheimers Research and Therapy
17589193 (eISSN)
Vol. 16 1 213-Subject Categories
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1186/s13195-024-01578-6
PubMed
39358810